Organiser: Professor Des Johnston (Heriot-Watt)
| Main Lecture Courses | Speaker | Institution |
| Introduction to quantum information and algorithms | Chiara Macchiavello | University of Pavia |
| Introduction to QML | Zoe Holmes | EPFL |
| Hamiltonian Simulation | Guglielmo Mazzola | University of Zurich |
| Plenary Talks | Speaker | Institution |
| Applications of quantum simulations and QML | Francesco Tacchino | IBM Zurich |
| QNNs - advanced theory | Amira Abbas | University of Amsterdam |
This LMS Research School for PhD students and early stage researchers is devoted to quantum machine learning (QML) and quantum simulations, which constitute active and very promising areas of research. Both QML and Hamiltonian simulations encompass interesting mathematical structures and give a gateway into deep, open questions in computational complexity and quantum advantage. Both topics have generated considerable recent interest because of their applicability on current and near-term noisy quantum hardware.
The three main lecture courses at the school reflect these concerns and opportunities. Introduction to quantum information and algorithms will introduce a mathematical audience to the basic concepts and key ideas that are used in constructing efficient quantum algorithms and measuring their effectiveness. The other two courses, Introduction to Quantum Machine Learning and Hamiltonian Simulation will look at the application of this framework in machine learning and the simulation of quantum systems and optimization.
Whilst the summer school is primarily designed to equip early career mathematicians with cutting-edge knowledge that will allow them to approach the current research landscape in QML, the courses and plenary talks will also address the wider industrial and societal impact of the research area.